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© Oxford University Press

Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology

Kimmen Sjölander 3, Kevin Karplus , Michael Brown , Richard Hughey , Anders Krogh 1, I.Saira Mian 2 and David Haussler

Baskin Center for Computer Engineering and Information Sciences, Applied Sciences Building, University of California at Santa Cruz Santa Cruz, CA 95064, USA
1The Sanger Centre, Hinxton Hall Hinxton, Cambs CB10 1RQ, UK
2Life Sciences Division (Mail Stop 29—100), Lawrence Berkeley Laboratory, University of California Berkeley, CA 94720, USA

1To whom correspondence should be addressed. E-mail: kimmen{at}cse.ucsc.edu

We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichiet mixture densities are designed to be combined with observed amino acid frequencies to form estimates of expected amino acid probabilities at each position in a profile, hidden Markov model or other statistical model. These estimates give a statistical model greater generalization capacity, so that remotely related family members can be more reliably recognized by the model. This paper corrects the previously published formula for estimating these expected probabilities, and contains complete derivations of the Dirichiet mixture formulas, methods for optimizing the mixtures to match particular databases, and suggestions for efficient implementation.


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